Citations

Abstract Objectives: Community-acquired intra-abdominal infections (CA-IAIs) are a leading cause of US hospitalizations. Piperacillin-tazobactam is often used to empirically treat CA-IAIs, despite national guidelines recommending narrower-spectrum antibiotics for these infections. The overuse of broad-spectrum agents such as piperacillin-tazobactam contributes to antibiotic resistance, which poses serious public health challenges. This resident-led quality improvement initiative aimed to reduce unnecessary piperacillin-tazobactam use for treating CA-IAIs measured as DOT/1,000 patient-days by 10% without adversely affecting hospital length of stay (LOS). Methods: Using the DMAIC (define, measure, analyze, improve, control) framework, we identified barriers to appropriate antibiotic use and developed a treatment algorithm for CA-IAIs that included clear guidelines and exclusion criteria. This algorithm was disseminated to internal medicine residents and emergency department physicians along with educational sessions to highlight updated CA-IAI treatment recommendations, antibiotic resistance, and appropriate antibiotic ordering via the electronic health record. Antimicrobial stewardship pharmacists provided overnight support to assist with de-escalation. Data were collected over a 10-month period spanning 2 intervention phases. The primary outcome was piperacillin-tazobactam use, measured as days of therapy (DOT) per 1,000 patient-days and DOT per patient LOS. Mean LOS served as the balancing measure. Results: Piperacillin-tazobactam use was significantly reduced ( P < .001) after the interventions without increasing the mean LOS. Conclusion: This project raised awareness of antibiotic resistance and led to lasting improvements in reducing the inappropriate use of piperacillin-tazobactam to treat CA-IAIs, without affecting the mean LOS. This was attributed to the strong collaboration among a multidisciplinary team of infectious disease physicians, antimicrobial stewardship team members, residents, emergency department physicians, and faculty.

ABSTRACT Aims To evaluate the real‐world associations between semaglutide 2.4 mg and cardiometabolic comorbidities, biomarkers and cardiovascular risk among adults with overweight or obesity. Methods This retrospective cohort study used US claims data and laboratory measurements from the Komodo Research Database (2016–2024). Adults with obesity or with overweight and ≥ 1 obesity‐related comorbidity were included. Patients on semaglutide 2.4 mg for ≥ 12 months were compared with non‐users (including users of other obesity medications) using propensity score weighting with residual imbalances adjusted in regression model. Outcomes included time to onset of cardiometabolic comorbidities, change in cardiometabolic biomarkers and changes in 10‐year ASCVD risk score at 12 months. Results 25 783 semaglutide 2.4 mg users and 8 948 913 non‐users were identified. Semaglutide 2.4 mg was associated with significantly lower risks for incident prediabetes or type 2 diabetes, type 2 diabetes only, hypertension, ASCVD, heart failure and CVD (HRs 0.45–0.75; all p < 0.05), as well as greater improvements in BMI (−4.1 kg/m 2 ), SBP (−6.2 mmHg), HbA1c (−0.4%), LDL cholesterol (−7.7 mg/dL), triglycerides (−28.6 mg/dL) and HDL cholesterol (+1.9 mg/dL; all p < 0.001). At 12 months, semaglutide 2.4 mg users ( N = 161) demonstrated a greater reduction in ASCVD risk score (−0.7% vs. +0.3%; between‐group difference −1.0%, p < 0.001), with a decrease in the proportion at intermediate‐high ASCVD risk (14.9%–9.3% vs. 17.7%–18.7%; OR 0.3 [95% CI: 0.1, 0.6], p < 0.001). Conclusions Semaglutide 2.4 mg was associated with lower risk of multiple cardiometabolic comorbidities, favourable changes in biomarkers, and lower estimated ASCVD risk in this population.

Abstract Unsupervised molecular clustering is fundamental to precision medicine, yet most published subtyping studies lack standardized validation criteria, making it impossible to distinguish biological signal from statistical artifact. We present the Pathway Subtyping Framework, which enforces four mandatory validation gates—label shuffle, random gene set, bootstrap stability with adaptive thresholds, and ancestry independence—before any clustering result is reported. On TCGA-COAD (n=452), the framework independently recovered CMS4 (Mesenchymal) with 75.9% CMS4 enrichment (Fisher OR=16.71, p=1.4×10⁻²⁵), outperforming PCA+k-means (67%), gene-level k-means (50%), and NMF (32%) on the same data. Applied to autism (n=56) and schizophrenia (n=281) as pilot demonstrations, the framework produced replicable subtypes across platforms (ARI=0.870) and achieved the strongest gate performance across all datasets (bootstrap ARI=0.923). Cross-disease analysis reveals that molecular subtypes are independent of DSM diagnosis (Cramér's V=0.036) but strongly dependent on brain region (V=0.528); within-region concordance is 84% (ARI=0.862), decomposing the 87% global concordance into disease-agnostic and anatomical components. The adaptive bootstrap threshold model, calibrated on 47 independent benchmarks (R²=0.889, LOOCV RMSE=0.051), replaces arbitrary fixed thresholds with data-driven, silhouette-calibrated gates. The framework is open-source (PyPI, Docker, Codeberg; 1,054 tests; DOI:10.5281/zenodo.18867165).

Abstract Background : Traumatic encephalopathy syndrome (TES) is a clinical research construct used to identify individuals at risk for chronic traumatic encephalopathy (CTE) following exposure to repetitive head impacts (RHI). Adjudication of TES relies on clinical features such as progressive cognitive impairment and neurobehavioral dysregulation. Blood-based biomarkers and structural neuroimaging abnormalities have been associated with TES but are not part of the criteria. This study evaluated whether TES identification was associated with the combined contribution of cognitive performance, blood biomarkers, and structural neuroimaging measures across two well-characterized cohorts. Methods : Participants included 158 professional fighters from the Professional Athletes Brain Health Study and 149 former American football players from The DIAGNOSE CTE Research Project. Three indices were constructed representing complementary domains: a cognitive index reflecting cohort-specific cognitive features, a blood biomarker index including plasma neurofilament light chain, glial fibrillary acidic protein, total tau, tau phosphorylated at amino acid 231, and APOE -ε4 carrier status, and an imaging index comprising volumetric MRI measures of subcortical structures, ventricles, and corpus callosum subregions. Grouped weighted quantile sum regression models were estimated within each cohort to evaluate associations between these indices and TES while adjusting for age, race, competition status, and RHI exposure. Results : Multidomain models demonstrated improved model performance compared with single-domain models in both cohorts (PABHS: AUC=0.91, PPV=0.80; DIAGNOSE CTE: AUC=0.84, PPV=0.85). Biomarker and imaging indices contributed additional information across cohorts, although imaging contributions were more prominent in fighters whereas blood biomarker associations were stronger in football players. Conclusion : TES in RHI-exposed athletes was associated with a convergent clinicobiological profile observed across two independent cohorts with distinct exposure patterns. These findings support multidomain analytic frameworks for evaluating correlated biological signals in RHI-exposed populations and may inform future studies of TES and CTE.